Question

How can I optimize a SUMX() function that is performing poorly on large datasets?
My Power BI report contains a SUMX function applied to a large dataset, causing slow performance. What are the best optimization techniques, such as reducing row iteration, leveraging aggregations, or restructuring the data model, to improve efficiency?

Answer 1

To optimize a SUMX() function for large datasets in Power BI, follow these best practices:

1. Reduce Row Iteration with Pre-Aggregation

Instead of iterating over every row, pre-aggregate values in a summarized table before applying SUMX().
Example:

Optimized Sales = 
SUMX( 
    VALUES( 'Sales'[ProductID] ), 
    CALCULATE( SUM( 'Sales'[Revenue] ) ) 
)

This improves row iteration by aggregating at higher granularity using SUM.

Use measures instead of calculated columns.

Any column used in SUMX() should be changed to a measure so SUMX() can run more efficiently.

Calculations carried out row by row through calculated columns are volatile, so it is best to avoid them.

3. Maximize Dependencies on High Cardinality Columns

High cardinality columns, such as unique transaction IDs, slow down the performance of SUMX().

Try grouping data at a higher level so as to minimize row counts that are involved in processing.

4. Use variables to restrict repetitive calculations

To eliminate repetition, precomputed values are put into variables within SUMX(). It keeps your code a lot neater and helps avoid performance hits.

Example:

Optimized Sales = 
VAR RevenuePerRow = SUM( 'Sales'[Revenue] )
RETURN 
SUMX( VALUES( 'Sales'[Category] ), RevenuePerRow )

Therefore, this stops SUMX() from doing calculations over and over for every individual row.

5. Consider Other Aggregations (SUM Instead of SUMX)

Use SUM() when appropriate because it operates directly on a column and is faster.

Another approach would be to try SUMMARIZE()/GROUPBY() to pre-compute before applying SUMX().

6. Optimize Data Model & Storage Mode

Adopt the Star Schema and dare not perform unnecessary relations.

In the case of DirectQuery, consider importing data that is accessed frequently.